1. Technical Field
The present invention relates to an image processing apparatus, an image processing method, a processing apparatus, a processing method, and a recording medium.
2. Related Art
A known image filter generating method uses evolutionary computation such as genetic algorithms or genetic programming (for example, see Non-Patent Document 1). This technique generates new image filters by repeatedly performing genetic operators such as crossover, mutation and selection on image filters multiple times. Such an image filter generating method based on evolutionary computation can reduce the amount of work required for designing optimal image filters for individual cases, which are difficult to be obtained analytically due to their complex configurations.
Non-Patent Document 1: Masaki MAEZONO et al., “Research on the Design of an Image Filter based on a Generic Algorithm”, [online], Council for Improvement of Education through Computers, [Searched on Mar. 20, 2008], Internet <URL:http://www.ciec.or.jp/event/2003/papers/pdf/E00086.pdf>
When an image filter is generated by means of evolutionary computation, generation alternation repeatedly occurs before the target image filter is obtained. In each generation, a to-be-converted image is converted by using a plurality of image filters so that a plurality of to-be-selected images are generated. The to-be-selected images are compared with a target image. As a result of the comparison, one or more of the image filters are selected which can produce to-be-selected images that are more similar to the target image than the others. The selected image filters survive to the next generation.
As the generations proceed, an increasing number of the to-be-selected images generated by using the image filters are similar to the target image. In other words, the to-be-selected images only slightly differ from each other, which makes it difficult to distinguish image filters that should survive to the next generation from image filters that should perish in the current generation.
Therefore, after a certain number of generation alternations, the comparison between the to-be-selected images and the target image is preferably performed with different weights being assigned to a plurality of regions defined in each image. In this way, it becomes possible to easily determine whether the to-be-selected images are similar to the target image and to increase the accuracy of the image filter selection. For example, the comparison result obtained for an important region in each image is heavily weighted, and the comparison result obtained for an insignificant region in each image is lightly weighted. This weighting scheme can select image filters that provide accurate filtering for an important region in each image. It, however, is very difficult to determine appropriate weights for individual cases since it requires extensive experience and knowledge.
In addition to image filters, general data converters can similarly be generated by means of evolutionary computation. It is also very difficult to assign appropriate weights and select accurate data converters since it requires extensive experience and knowledge.
Here, a user of an image filter selected in the last generation is not allowed to check the weights assigned to the respective regions during the comparison between the to-be-selected images and the target image in each generation. In other words, the user cannot correct the process of determining the weight assigned to each region in the generations before the last generation, for example.
If the importance of each region is determined incorrectly in the images, an appropriate image filter cannot be selected even after the last generation is completed. If such occurs, the user of the image filter selected in the last generation is required to determine the weights to be reassigned to the individual regions and then restart the filter selecting procedure from the first generation.